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The changes of total vegetation cover (VC) (km 2 ) and wheat production (1000 MT) of the Al-Kharj. The three RS dates, 1993, 1998, and 2001, were for Landsat-5 from Modaihsh et al. (2015). The 2007 image was from the Landsat Thematic Mapper from Algahtani et al. (2015).
Source publication
Vegetation cover (VC) change detection is essential for a better
understanding of the interactions and interrelationships between humans and
their ecosystem. Remote sensing (RS) technology is one of the most beneficial
tools to study spatial and temporal changes of VC. A case study has been
conducted in the agro-ecosystem (AE) of Al-Kharj, in the c...
Context in source publication
Context 1
... an at- tempt to explicate the reason for the ecosystem's total VC decrease in the last decade, a relationship between total VC and wheat production has been depicted. Figure 7 shows a di- rect relationship between wheat production in Saudi Arabia (USDA, 2015) and total VC in Al-Kharj AE. Furthermore, it recorded evidence of progressive increase of wheat produc- tion and total VC during the period of 1984-1993(Algahtani et al., 2015Modaihsh et al., 2015;USDA, 2015). ...
Citations
... Over the years, West Africa, with a particular focus on Ghana, has witnessed an expansion of farming and pasture areas aimed at meeting the food, meat, and raw material demands of a growing population in the sub-region. This expansion is likely to adversely impact the ecosystem, vegetation, water, and soil quality, potentially leading to increased conflicts between crop farmers and animal herders (Aly et al., 2016;Bin, 2008). The terms Land Use and Land Cover (LULC) denote distinct concepts illustrating the interaction between humans and the natural land surface. ...
... This therefore makes the analysis of LULC changes, especially in areas of intensive agricultural production activities very paramount. This is crucial due to the fact that the examination of changes in land use and land cover allows for the acquisition of data concerning untouched lands, the conversion or intensification of agricultural areas, and deforestation (Aly et al., 2016;Fu et al., 2023;Yiran et al., 2012). This information holds particular significance because the rate of land cover change in Africa is surpassing expectations, driven by population growth and the imperative to expand and intensify farming and herding for increased food production to sustain the growing populace. ...
... These activities are likely to intensify and further worsen the already fast-changing land use land cover (LULC) dynamics due to population surge and the rising demand for food. The potential land use intensity implies that more land cover is likely to be converted as a result of the expansion of farmlands and increasing herd population and demand for grazing space, water, settlement, and urban development (Aly et al., 2016;Bessah et al., 2019). These activities are the major anthropogenic causes of changes in land use land cover (LULC) which may have an impactful effect on climate change (Bessah et al., 2019;Mekasha et al., 2014). ...
This study was conducted to investigate the effect of farming and pasture area extensions on land use and land cover in the North Eastern Corridor of Ghana. Landsat 5 TM + image period of 2000 and Landsat 8 TOA satellite for the periods 2013 and 2022 were used. All images were captured at approximately the same period to ensure the selected images had the same reflectance values. A supervised machine learning technique using the algorithm of the random forest classifier was employed for the preparation of the classification of thematic maps. The Markov chain model was used to examine the dynamics of land use and land cover (LULC) changes in the study area. Visual appraisal of the images indicated some level of notable changes across the various classes from 2000 to 2022. The trend of the various changes in percentage terms also supports this observation. The results reveal that there was an improvement in the vegetation cover from 2000 to 2013 as reflected in the maximum and median NDVI values of the classified images within the period. However, the results show a considerable decline in vegetation health from 2013 to 22. Based on these results, we recommend that a more in-depth analysis to identify other possible anthropogenic activities and factors, that may serve as significant underlying causes of these vegetation cover changes in the region. The Ministry of Food and Agriculture (MoFA) should train farmers to incorporate tree planting into their farming whilst avoiding deforestation and bush burning within the area. ARTICLE HISTORY KEYWORDS land use land cover change; extension of farming; markov chain model; normalized difference vegetation index; remote sensing data
... Vegetation constitutes a critical component of terrestrial ecosystems, playing a significant role in material cycling, energy flow, and information exchange [1,2]. As an integral part of the ecosystem, vegetation provides numerous services essential for human survival [2][3][4]. Influenced by factors such as topography, soil, climate, and human activities, vegetation change serves as a key indicator of regional or global environmental transformations [5][6][7]. Monitoring vegetation changes is crucial for understanding regional climate change characteristics and the extent of human interference [8][9][10][11][12]. ...
... Since the early 1970s, the ongoing missions of the Landsat satellites have captured surface landscape information for over half a century [25], providing lots of remote sensing imagery with high spatial and spectral resolutions [1,3,26]. The Landsat series satellite images are among the most powerful data sources for studying large-scale and long-term spatiotemporal changes in vegetation [27]. ...
... Seasonal variations often introduce errors in landscape extraction and change detection [29]. To mitigate this, we used cloud-free images collected exclusively during the autumn seasons of four selected years (1987,1997,2007, and 2017) for vegetation extraction, thereby avoiding uncertainties caused by seasonal changes [3,30]. In this study, we established an interpretive sign information database and selected training samples by combining field survey data and historical imagery from Google Earth. ...
Vegetation and its spatiotemporal variations play a crucial role in regional ecological security and sustainable development. Examining vegetation dynamics in natural reserves provides valuable insights for optimizing vegetation patterns and management strategies. This study utilizes Landsat remote sensing imagery to investigate changes in vegetation pattern and coverage in the Cangshan mountain of the Cangshan Erhai National Nature Reserve, as well as assesses the effectiveness of conservation efforts. The results indicate the following: (1) The primary vegetation types in the Cangshan mountain include warm-temperate coniferous forests, deciduous broad-leaved forests, bamboo forests, and alpine meadows, exhibiting distinct vertical zonation patterns. The vegetated area expanded by 1146 hectares during the study period. (2) The average fractional of vegetation coverage (FVC) in the Cangshan mountain demonstrated an upward trend (0.82 in 1987 to 0.93 in 2017), with the proportion of highly FVC areas increasing from 59.67% in 1987 to 97.89% in 2017. (3) The vegetation landscape fragmentation in Cangshan mountain and various functional areas shows an increasing trend, while connectivity decreases, and is accompanied by a more intricate shape of the vegetation landscape. While conservation and management efforts have yielded certain results in safeguarding the vegetation in the Cangshan mountain, the degree of vegetation landscape fragmentation has intensified due to climate change and human activities. Thus, it is imperative for management authorities to promptly adjust protective measures within the Cangshan mountain. This study contributes to our understanding of vegetation changes within the Cangshan mountain and provides essential baseline information for optimizing and enhancing vegetation conservation management strategies within the reserve.
... Higher values designate the finer and salubrious vegetation, but values nearer to 0 and − 1 signify unfertile land and water bodies, respectively [2],Nega 2019). The formula of NDVI is given below in Eq. (1) In NDVI, the bright parts denote the high vegetation region, whereas the dusky zones represent the low vegetation region [4]. ...
Vegetation cover refers to the proportion of soil covered by green plants. It helps maintain an ecological balance. The present study monitors vegetation cover of Shali River basin, which is enclosed with five protected forests from 1995 to 2020 using remote sensing and geographical information system (GIS). The entire study has been organized into three phases. In the first phase, vegetation cover trend analysis has been performed using the normalized difference vegetation index (NDVI) from 1995 to 2020. Along with vegetation change analysis also conducted from 1995 to 2010 and 2010 to 2020. In the second phase, health conditions using vegetation health index (VHI) and correlation analysis have been done. In the third phase impact of vegetation cover on land surface temperature (LST), rainfall and air quality have been analyzed. The results show a positive trend in vegetation coverage. Change analysis indicates that, from 1995 to 2010, Shali basin has 15.81 km2 dense vegetation cover. While from 2010 to 2020, the Shali basin has 33.81 km2 dense vegetation cover. The correlation between vegetation cover and health condition is showing a positive relation but the percentage of correlation has been reduced from 1995 to 2020. Lastly, the study shows a positive correlation between vegetation cover and rainfall, while negative correlation in LST and air quality. The proposed method can be used for seasonal vegetation cover change analysis. Globally, the decision and policymakers should put emphasis on the application of remote sensing techniques in vegetation cover monitoring to attain ecological balance.
... LAI has a lot of advantages. Some of these include the fact that they are employed in agricultural and ecological studies for a variety of objectives, such as yield evaluation, stress determination, and primary productivity, which are connected to photosynthesis, transpiration, respiration, and the carbon and nutrient cycles [19]. Thus, LAI is a crucial input for a variety of agricultural, ecological, climatic, and hydrological models, including models of canopy photosynthesis, crop growth, transpiration, precipitation, evaporation, and primary production. ...
... An important drawback of LAI is that it cannot be used to quantify stress on each plant species since different plants grow together as a community. LAI relates to the Soil Adjusted Vegetation Index (SAVI), and their relationship is depicted in equation 1 [19]. Due to the lack of soil, the SAVI minimizes the brightness reflection with the Normalized Difference Vegetation Index (NDVI). ...
The Leaf Area Index (LAI) is an important algorithm for studying the health status of vegetation. In this study, the impact of hydrocarbon micro-seepage on vegetation in Ugwueme was investigated using the LAI image classification approach. Landsat TM 1996, ETM+ 2006, and OLI 2016 satellite images that were acquired from the United States Geological Survey (USGS) portal were used to classify various LAI maps as low, moderate, and high classes. The spatial–temporal analysis revealed that the low, moderate, and high LAI density classification changed, respectively, from 41.24 km2 (50.43%), 33.98 km2 (41.54%), and 6.56 km2 (8.02%) in 1996 to 23.70 km2 (28.98%), 29.48 km2 (36.04%), and 28.60 km2 (34.97%) in 2006, and to 38.23 km2 (46.74%), 27.54 km2 (33.68%), and 16.01 km2 (19.58%) in 2016. The stimulation analysis shows that by 2030 (the 14-year planning period), the low, moderate, and high LAI density classifications will be 8.86 km2 (10.82%), 24.28 km2 (29.70%), and 48.63 km2 (59.46%), respectively. The study shows that LAI is an important algorithm that can be effectively used to study the health status of vegetation in an ecosystem.
... Low recharge rates and degraded groundwater quality pose a serious threat to ecosystem services, particularly in periods of water scarcity [1][2][3]. Due to lower precipitation and harsh climatic conditions, the groundwater deterioration rates are higher in the Kingdom of Saudi Arabia (KSA) [4][5][6]. Climate change affects groundwater quality and quantity in many direct and indirect ways [6][7][8]. Xiaolong and Boufadel [9] and Ludwig and Moench [10] The initial SALTMED model version was used during 2000-2002 for data on tomato fields in Syria and Egypt. ...
Groundwater depletion coupled with climate change, increasing temperature, and decreasing precipitation, has led to groundwater quality deterioration and diminishing groundwater quantity, subsequently affecting agricultural productivity in arid environments. The groundwater of the Al-Baha region, Saudi Arabia is located in unconfined shallow aquifers and responds quickly to climate change. The Al-Baha region is facing an increase in temperature and a substantial decrease in precipitation. Over the 24-year period from 1995 to 2019, average temperatures increased by 1.1 °C–1.6 °C, while rainfall decreased by 24–41%. Consequently, this study aimed at investigating the influence of climate change on soil salinity and pomegranate productivity. To achieve this goal, a hundred and fifteen samples of soil and groundwater were collected from different locations in the Al-Baha region. Furthermore, the SALTMED model was calibrated using the salinities of 50 groundwater samples, which are used as irrigation water, and climatic data from the year 2020. The model was then validated using 65 irrigation water salinities and climatic data from the year 2020. Pomegranate fruit yield was used as the main variable for calibration and validation. After successful calibration and validation, the SALTMED model was run using ‘what if’ scenarios for the years 2044, 2068, and 2092. It is assumed that the temperature will increase, while the annual rainfall will decrease in upcoming decades. Consequently, the groundwater salinities will reach 1.44, 2.59, and 4.67 dS m−1 for the years 2044, 2068, and 2092, respectively. The results revealed that the soil salinities will increase by 113%, 300%, and 675%, respectively, compared with the average soil salinity of the year 2020 (2.22 dS m−1). Furthermore, the pomegranate tree productivity in the Al-Baha region will decrease significantly (24.0%, 36.6%, and 41.6%) in the predicted three years due to deterioration of groundwater quality and increasing temperatures. Interventions by the regional authorities to minimize the impact of climate change on crop and fruit productivity and groundwater deterioration in the Al-Baha region should be planned and carried out as soon as possible. The method used in this investigation can be utilized in similar ecosystems worldwide.
... Este último determinó un resultado favorable con aumento de la cubierta vegetal gracias a los esfuerzos de conservación. En Egipto (Al-Kharj), Aly et al. (2016), con imágenes Landsat, determinaron un aumento de la cobertura vegetal (CV), en ecosistemas áridos (107,4% entre 1987 y 2000) y disminución de la misma (27,5% entre el 2000 y 2013), producto del deterioro y salinización del suelo y el agua. En Colombia, Muñoz-Guerrero et al. (2009) evaluaron la dinámica de cambio entre 1989 y 2008 en la microcuenca Las Minas (Pasto, Nariño), con imágenes Landsat10TM e IKONOS; los resultados permiten discriminar la reducción del bosque secundario (58,51 ha) con una tasa de deforestación de 3,08 ha/año, un aumento de áreas destinadas para la siembra (141,64 ha) por presiones de deforestación y un aumento de actividad bovina y pecuaria. ...
En los últimos años ha existido un avance significativo en los Sistemas de Información Geográfica (SIG) y en el desarrollo de métodos de clasificación supervisada, hasta ahora estos no habían sido utilizados para calcular con exactitud la extensión superficial de páramos en sectores de la Cordillera Oriental de Colombia y mucho menos para estimar la distancia entre los límites de esos páramos y los principales rasgos geológicos. Por esta razón, en la presente investigación se evaluaron cinco métodos de clasificación supervisada, con el propósito de determinar cuál de estos posee una mayor resolución para reproducir la extensión y distribución superficial de los páramos de Merchán y Telecom en Saboyá, Boyacá, pertenecientes al Complejo de Páramos Iguaque - Merchán en la Cordillera Oriental de Colombia. Con esta finalidad, se escogieron imágenes satelitales del área de estudio por medio de Landsat 8 para el año 2018 y se clasificaron utilizando algunos algoritmos basados en Machine Learning (SVM, RF, DT, BC y ANN). Para establecer la exactitud y confiabilidad de los datos de clasificación de las características del terreno se calculó el Índice Kappa, que permitió determinar que el método más preciso para este caso fue RF. Adicionalmente, dado que los límites de los páramos coinciden con estructuras geológicas o contactos entre formaciones, se estimó la distancia entre el borde de los páramos y esos rasgos. Los resultados obtenidos en esta investigación son considerados como insumo para futuros análisis multitemporales, y estimación de distintas sirven como herramienta para la elaboración y toma de decisiones en la gestión de recursos naturales, biodiversidad, prestación de servicios ecosistémicos, y ordenamiento territorial para el municipio de Saboyá-Boyacá.
... Time series remote sensing is an invaluable resource for dynamic monitoring of the environment over short and long time spans. 1 This is because of the ability of remote sensors to cover a large area in a short period of time as well as their capability to revisit and acquire data for the exact area, which optimises environmental monitoring of large areas based on time series image analysis. [2][3][4] In other words, as stated in Ghauri and Zaidi 5 , 'remote sensing provides continuous monitoring and mapping, both spatial and temporal, as opposed to a limited frequency point measurement'. Furthermore, rugged and hilly terrains can be expensive and cumbersome to access for point measurements. ...
... The most typically used techniques for vegetation change detection include principal component analysis, minimum noise fraction, tasselled cap, Normalised Difference Vegetation Index (NDVI) and supervised image classification. [2][3][4][16][17][18] Supervised image classification is the process of clustering pixels in an image into classes corresponding to user-defined training classes. 19 The accuracy of this classification method depends heavily on the quality of training sites and the spectral distinctness of the classes. ...
... As a result, the NDVI has been widely used in monitoring the change in vegetation cover and vegetation health and productivity. [2][3][4]17,18,24 The NDVI is mathematically expressed as the difference between NIR and red channels divided by their sum of them 23 : ...
This study presents a simple approach of spatiotemporal change detection of vegetation cover based on analysis of time series remotely sensed images. The study was carried out at Thathe Vondo Area, which is characterised by episodic variation of vegetation gain and loss. This variation is attributable to timber and tea plantations and their production cycles, which periodically result in either vegetation gain or loss. The approach presented here was implemented on two ASTER images acquired in 2007 and 2017. It involved the combined use of band combination, unsupervised image classification and Normalised Difference Vegetation Index (NDVI) techniques. True colour composite (TCC) images for 2007 and 2017 were created from combination of bands 1, 2 and 3 in red, blue and green, respectively. The difference image of the TCC images was then generated to show the inconsistencies of vegetation cover between 2007 and 2017. For analytical simplicity and interpretability, the difference image was subjected to ISODATA unsupervised classification, which clustered pixels in the difference image into eight classes. Two ISODATA derived classes were interpreted as vegetation gain and one as vegetation loss. These classes were confirmed as regions of vegetation gain and loss by NDVI values of 2007 and 2017. In addition, the polygons of vegetation gain and loss regions were created and superimposed over the TCC images to further demonstrate the spatiotemporal vegetation change in the area. The vegetation change statistics show vegetation gain and loss of 10.62% and 2.03%, respectively, implying a vegetation gain of 8.59% over the selected decade.
Significance:
• Vegetation change detection is essential in environmental monitoring and management of an area. This study presents a simple approach for assessing vegetation change over time. The approach involves change detection through
... Remote sensing allows for cost-and time-efficient monitoring of landscapes vital to the conservation of natural resources, ecosystems, and biodiversity [3]. Vegetation cover change detection is essential for a better understanding of the interactions and inter-relationships between humans and their ecosystem [14]. Remote sensing provides fast and robust methods to rapidly assess landscape ecological changes based on spectrally-derived vegetation indices. ...
Mountains are amongst the landforms that have undergone the most transformation. Landscape changes in mountains are driven by anthropogenic stressors and climatic change. The UN Sustainable development Goal 15 recognized the importance of conservation of mountain ecosystems for an enhancement of sustainable development. This study seeks to evaluate spatio-temporal ecological changes in the Northeastern Bamenda Highlands, based on a remote sensing-derived mountain green cover index proxy, the Normalized Difference Vegetation Index (NDVI). The study showed vegetation greening and browning trends exemplified by degraded montane forest linked to anthropogenic stressors and natural climatic shift. These anthropogenic stressors include deforestation, conversion of forest to farmlands and eucalyptus plantations, and the unsustainable grazing with inter-annual use of fires for pasture regeneration. As a means to ensure future ecological services provision of these highlands, landscape restoration strategies are needed. The greening of the highlands with water retaining trees species, sustainable grazing and farming restrictions in protected areas and its buffers.
... The normalized difference vegetation index (NDVI) has been widely used as a useful indicator to perceive the information of vegetation response at a global and regional scale (Aly et al., 2016;Gong et al., 2015;Jiang et al., 2013;Li et al., 2013;Tucker, 1979;Van Eck et al., 2016). Nowadays, many NDVI datasets are available from remote sensors using spectral reflectance of near-infrared (NIR) and red (RED) bands (Chen et al., 2014;Piao et al., 2011;Wang et al., 2011;Zhou et al., 2001). ...
Field-based investigations of land use/cover changes are time-consuming and challenging for large areas, where short and long-term changes in climatic and hydrologic variables affect mainly ecosystem services. Thus, there is a substantial demand to boost the new modeling framework and employ remote sensing capabilities to quantify hydro-climatic impacts on land dynamics. In this study, a conceptual framework has used to assess the climatic land greening, climatic land degradation, non-climatic (hydrological) land greening, and non-climatic (anthropogenic) land degradation responses with hydro-climatic variables under dry and wet spells' effect in the Betwa River Basin (BRB) of central India. Remotely sensed MODIS (NDVI and land cover) time-series datasets had been used to quantify spatiotemporal changes in major land use/cover classes. The standardized coefficients for rainfall (β = 0.62) and relative humidity (β = 0.32) showed their high relative importance in the relationship analysis performed using Multiple Linear Regression (MLR). The result indicates that dominant agricultural crop-land has been significantly impacted by changes in maximum and diurnal temperature, which affirm degradation, and positively responded by changes in rainfall, minimum temperature, and aridity index, which demonstrate greening wet period. Spatial analysis showed that land degradation affecting ecosystem services had been variedly distributed from the upper to lower basin due to more climatic impacts than anthropogenic disturbances. Thus, the developed conceptual framework can be adopted to discover dynamic land consequences and understand their responses for sustaining ecosystem services in the dominant agricultural region.
... During the past three decades, a concern of climate change, soil crop production, and protection of environment has increased overdramatically [1][2][3]. More integrated research and investigation of sustainable technology concepts have been conducted. ...
... Furthermore, the biochar high porosity increases the capacity of soil to hold water at field capacity (FC) and decrease soil bulk density [12][13][14]. The Kingdom of Saudi Arabia (KSA) has a climate that ranges from arid to semi-arid and most of the soils are sandy, containing high amounts of calcium carbonate due to both high temperatures and lack of precipitation [1]. The KSA soils are on hydro-physical properties of a sandy soil. ...
In this study, waste olive leaves and branches were pyrolyzed to produced biochar, and their impacts on physical and chemical properties of a sandy soil were evaluated. Pyrolytic temperatures of 300 °C, 400 °C, and 500 °C were used for biochar production. After evaluating the physio-chemical properties, the produced biochars were added to the top 10 cm layer of the soil at rates of 0%, 1%, 3%, and 5% in a column experiment at 25 °C. Biochar was mixed with a sandy soil into the top 10 cm of the columns. For all treatments, cumulative evaporation was reduced; however, treatments with 5% biochar prepared at the highest temperatures showed the highest impact. The available water contents were increased by 153.33% and 151.11% when olive branch-derived biochar and olive leaves-derived biochars produced at 500 °C were applied at 5% rate, respectively. No impact of available water was observed for 1% biochar contribution. Biochar application decreased both cumulative infiltration and infiltration rate. Biochar pyrolyzed at 500 °C most intensely improved hydro-physical properties of a sandy soil. However, its application as a soil supplement in arid environments should be adopted with constraints due to its high pH (9.69 and 9.29 for biochar pyrolyzed at the highest temperatures) and salinity (up to electrical conductivity = 5.07 dS m−1). However, the salinity of biochar prepared from olive branches (5%, pyrolyzed at 500 °C) was low (0.79 dS m−1); therefore, it can be used safely as a supplement in saline and acidic soils, but with restriction in alkaline soils.
